Ar#ficial Intelligence

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Doris Doyle
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1 Ar#ficial Intelligence Lecture 2 Vibhav Gogate The University of Texas at Dallas Some material courtesy of Luke Zettlemoyer, Dan Klein, Dan Weld, Alex Ihler and Stuart Russell

2 Announcements Project 0 is due next Thursday via e- learning Project 1 will be out next Thursday Due in 2 weeks A bit lengthy, start early

3 Computa#onal Ra#onality Intelligent Agents This course is about Designing Ra*onal Agents Informally, Agent is something that acts Informally, Ra*onality = Intelligence!! Take the best possible ac#on in a par#cular situa#on Example?

4 Agents: Defini#on An agent is anything that can be viewed as perceiving its environment through sensors and ac#ng upon that environment through actuators Agent Sensors? Percepts Environment Actuators Ac#ons

5 Agents An agent is anything that can be viewed as perceiving its environment through sensors and ac#ng upon that environment through actuators Human agent: Sensors: eyes, ears, Actuators: hands, legs, mouth

actuators Human agent: Sensors: eyes, ears, Actuators: hands, legs,

6 Agents An agent is anything that can be viewed as perceiving its environment through sensors and ac#ng upon that environment through actuators Human agent: Sensors: eyes, ears, Actuators: hands, legs, mouth Robo#c agent Sensors: cameras, range finders, Actuators: motors

7 Ra#onal (Intelligent) Agents An agent s intelligence is captured in a program Sensors are inputs Actuators are output A rational agent selects ac#ons that maximize its utility function. Agent Sensors? Actuators Percepts Ac#ons Environment U#lity func#on is an indirect specifica#on of what the program should do!

8 Intelligent Agents: Capabili#es Ability to interact with the real world to perceive, understand, and act speech recogni#on and understanding and synthesis image and video understanding ability to take ac#ons, have an effect Knowledge Representa*on, Reasoning and Planning modeling the external world, given input solving new problems, planning and making decisions ability to deal with unexpected problems, uncertain#es

9 Intelligent Agents: Capabili#es Learning and Adapta*on Modify and augment ini#al configura#on (background knowledge) as the agent gains experience Humans con#nuously learning and adap#ng; update models e.g. a baby learning to categorize and recognize animals Autonomy It should learn the compensate for par*al or incorrect background knowledge

10 Specifying a Ra#onal Agent Environment Percepts Ac#ons Ra#onality performance measure Agent Sensors Agent Program The? : Map percepts to ac#ons to maximize the performance measure? Actuators Percepts Ac#ons Environment

11 Vacuum World: Example Toy Agent Agent Sensors? Percepts Environment Actuators Ac#ons Environment: Squares A and B, Dirty or Clean Percepts: loca#on, contents e.g., [A, dirty] Ac#ons: {le`, right, vacuum, }

12 Ra#onality: Performance measure Fixed performance measure U#lity, Cost, Reward func#on Evaluate the ac#on & environment sequence One point for a totally clean house? One point per clean square per #me? Penalize for moves? One point per square vacuumed? Ra#onal agent Maximize expected performance given precept sequence to date Fairly general defini#on (due to generality of u#lity func#on)

13 Specify a Ra#onal Agent: Another example Consider an automated taxi system Performance measure? Environment? Actuators? Sensors?

14 Specify a Ra#onal Agent: Another example Consider an automated taxi system Peformance measure? Safety, des#na#on, profits, legality, comfort, Environment? City streets, freeways; traffic, pedestrians, weather, Actuators? Steering, brakes, accelerator, horn, Sensors? Video, GPS / naviga#on, keyboard,

15 Types of Environments Fully observable vs. par#ally observable Single agent vs. mul#agent Determinis#c vs. stochas#c Episodic vs. sequen#al Discrete vs. con#nuous

16 Fully observable vs. Par#ally observable Can the agent observe the complete state of the environment? vs.

17 Single agent vs. Mul#agent Is the agent the only thing ac#ng in the world? vs.

18 Determinis#c vs. Stochas#c Is there uncertainty in how the world works? vs.

19 Episodic vs. Sequen#al Do previous ac#ons affect your future ac#ons? Agent s experiences are divided into episodes. Ac#ons in one episode do not depend on ac#ons in others Current decision could affect all future decisions

20 Discrete vs. Con#nuous Is there a finite (or countable) number of possible environment states? vs.

21 Environment types PROBLEM Obs? Stoch? Seq? Dyn? Cts? Multi? Crossword puzzle?????? Poker?????? Medical diagnosis?????? Image analysis?????? Taxi driving??????

22 Environment types PROBLEM Obs? Stoch? Seq? Dyn? Cts? Multi? Crossword puzzle Fully Deter. Seq. Static Discrete Single Poker?????? Medical diagnosis?????? Image analysis?????? Taxi driving??????

23 Environment types PROBLEM Obs? Stoch? Seq? Dyn? Cts? Multi? Crossword puzzle Fully Deter. Seq. Static Discrete Single Poker Partial Stoch. Seq. Static Discrete Multi Medical diagnosis?????? Image analysis?????? Taxi driving??????

24 Environment types PROBLEM Obs? Stoch? Seq? Dyn? Cts? Multi? Crossword puzzle Fully Deter. Seq. Static Discrete Single Poker Partial Stoch. Seq. Static Discrete Multi Medical diagnosis Partial???? Single Image analysis?????? Taxi driving??????

25 Environment types PROBLEM Obs? Stoch? Seq? Dyn? Cts? Multi? Crossword puzzle Fully Deter. Seq. Static Discrete Single Poker Partial Stoch. Seq. Static Discrete Multi Medical diagnosis Partial???? Single Image analysis Fully Deter. Epis. Static Cts. Single Taxi driving??????

26 Environment types PROBLEM Obs? Stoch? Seq? Dyn? Cts? Multi? Crossword puzzle Fully Deter. Seq. Static Discrete Single Poker Partial Stoch. Seq. Static Discrete Multi Medical diagnosis Partial???? Single Image analysis Fully Deter. Epis. Static Cts. Single Taxi driving Partial Stoch Seq. Dyn. Cts. Multi Fluid; depends on exact problem / meaning The real world is typically partially observable, stochastic, sequential, dynamic, continuous, and multi-agent the most difficult set

27 Taxi example again Agent types Simple reflex Select ac#on based only on the current percept If car- in- front- is- braking then ini#ate- braking Model- based reflex Agents that keep track of the world If car- in- front- is- braking and recently- rained then ini#ate- braking requires internal state; memory

28 Goal- based More complex Agent types Ahempts to find a way to achieve some state If car- in- front- is- braking and needs to get to hospital then Search and planning: find path to goal state U#lity- based Most complex If car- in- front- is- braking and on fwy and needs to get to hospital alive then search of a way to get to the hospital that will make your passengers happy. Needs u"lity func"on : maps state to a real value (am I happy?) Can trade off: immediate vs future payoffs; risk vs reward

29 Summary What is Ar*ficial Intelligence? modeling humans thinking, ac#ng, should think, should act. History of AI Intelligent agents We want to build agents that act ra#onally Real- World Applica*ons of AI AI is alive and well in various every day applica#ons many products, systems, have AI components Reading Today: Ch. 1 & 2 in R&N For next week: Ch. 3 in R&N (search)

Agents and State Spaces. CSCI 446: Ar*ficial Intelligence Keith Vertanen

Agents and State Spaces. CSCI 446: Ar*ficial Intelligence Keith Vertanen Agents and State Spaces CSCI 446: Ar*ficial Intelligence Keith Vertanen Overview Agents and environments Ra*onality Agent types Specifying the task environment Performance measure Environment Actuators